Internet of Things (IoT) is a recent hot area in both academic and industrial interests, which has been proposed to address many application domains, including e-health, smart car, smart city, and smart home etc. While IoT paradigm allows to connect heterogeneous objects (e.g., sensors and/or devices), understanding their relationships can help to derive smart actions and useful predictions. In this research, we focus on action derivation from smart home by analysing Things relationships and their correlations. We consider re risk detection problem as a case study to evaluate di erent critical aspects of the approach, including precision, scalability, performance, and e ectiveness in terms of cost and complexity. We are currently working on building a simulation tool as a base of the study using some of the cutting-edge technologies, such as OWL ontology, semantic web services and data mining algorithms.
Internet of Things (IoT) is a promising evolution in the next big wave of web and
internet technologies. Under its vision, everyday objects (such as people, services,
devices, and sensors) need to be interconnected and smartly able to communicate
in a constructive and sensible ways to provide perfect services [
With respect to the latter (i.e., re protection service), the UK-government
reports [
In this research, we are generally interested in detecting unexpected actions in smart home by analysing Things relationships and their correlations. We seek to investigate the derivation techniques of smart actions in principle, and then practically evaluate their precision, scalability, performance, and e ectiveness in terms of cost and complexity. To this end, we apply our research on SH domain for detecting re incidences as a motivating case study. Currently, we are investigating some of the cutting-edge technologies to adopt, such as OWL ontology, semantic web services and statistical data mining algorithms.
The remainder of this paper is organised as follows. section 2 presents brie y work related to event based detection techniques. Sections 3 and 4 respectively present our research questions and proposal, focusing on SH application domain. Then, section 5 discusses the current status of our work and the plan for the next activities. Finally, section 6 concludes the paper. 2
Existing event-detection approaches fall roughly into one of the two categories:
ontology-based [
In SH, di erent detection activities are typically implemented, each to reach a
speci c goal. For instance, a well-SH system should provide healthcare services
by monitoring resident's movement or body condition. Whiles, other features
such as safety-service would require monitoring di erent entities in a di erent
mechanism such as observing daily habits (e.g., cooking) of home residents. [
To our knowledge, there are two closely relevant proposals to ours that
consider explicitly re detection problems [
SH supports integrating di erent devices and systems to be managed by a single control unit. It allows automating some actions without setting a timer or making an explicit request. Rather than monitoring or controlling home environment, SH needs to be more intelligent for generating smart actions, such as detecting unobvious re risk cases or notifying e ectively for future expectations in a dynamic way. Our main hypothesis is that \following the IoT paradigm by using the concepts and innovative technologies (such as OWL ontologies, semantic web services, statistical machine learning algorithms) would make home infrastructure more interactive, smart and aware in providing better/robust services". To shed lights on our objectives behind this hypothesis, we list two conceptual questions that may indicate the contributions we seek to make as follows: { What kinds of smart buildings an action prediction approach like we propose is useful for, and on which kinds of Thing's relationships this approach is more e ective? { What are the technical pros and cons of using IoT paradigm, including, complexity, scalability, performances, and usability?
Generally, these questions can be addressed by building up SH's simulation that allows to compare di erent risk scenarios with the ability of identifying the key pros and cons of the approach. Having such simulation would also help to explain whether following the IoT paradigm for smart home risk detection is bene cial as compared to the other alternative approaches.
To address the research questions of our research, we intend to examine two well-known techniques: (1) Ontology based structure to represent home model in a logical way, allowing to design entities, sensors, and their complex relationships. And (2) Machine learning to maximise home services by enabling some entities controlling automatically another entity without explicitly de ning prerequest for every action. These two methods are to ful l the main requirements of building a framework architecture for our research. This architecture would rely principally on three main components: a work ow simulation to simulate home entities in terms of generating input/output reading, controlled by a generic interface; a database schema designed by OWL-Ontology for storing all data; and an adapter to integrate our tool with a machine learning tool for encoding the recoded data as well as collecting inferred actions.
Progress so far. Fig. 1 describes brie y a suggested model for our SH. It consists of several connected entities to the local network, and each entity has a unique id such that its status (e.g., on/o ) can be checked. Sensor S3, as an example, is used to detect the presence of people inside the kitchen. Here, if the Cooker is o and nobody insides the kitchen, no risk can be detected unless something unusual occur, which can be detected by smoke or heat alarm. Even though it is highly recommended for people to stay in the kitchen while cooking, sometime people may forget to switch o their cooker before they leave. In response to this recommendation, if SH is capable for analysing the relationships between, e.g., Cooker and S3, optimising re risk detections in terms of alerting as early as possible for any expected risk can be achieved. Consequently, householders can take, in advance, further actions, e.g., going back to the kitchen or switching o the Cooker remotely, etc.
We have tried out to check conceptually the validity of our proposal, using the extracted dataset, see Fig. 2. In principle, this dataset describes the learning inputs such that the classes (i.e., de ned in Fig. 1) appear as columns, and each instance, representing reading data, appears as a row. In this preliminary experiment, we assume that the 28 instances (see, Fig. 2) are already extracted manually by de ning the behaviours of the entities, i.e., modelled in Fig. 1. For example, (rows from 1 to 21) describe some obvious cases that no risk event needs to be generated. They cover the cases when the Cooker is either o (rows 1 - 17), or in-use as normal by someone in the kitchen, indicated by S3 (rows 18 - 21). The rest of the rows cover some expected risk cases, including the absence of householders in the kitchen while the Cooker is ON (rows 22 - 27), or somebody already in but the utilisation of the Cooker exceeds the normal average time (row 28). The latter can be a target to fainting cases, especially for elderly people, in which it increases the level of their protection. Nevertheless, we intend to develop a home simulation system to generate interactively many instances for evaluating a variety of di erent scenarios. The bottom part of Fig. 2 illustrates the result obtained by RandomTree algorithm, graphically visualised as a decision tree of 7 nodes. This decision tree allows to determine whether a risk event must be triggered based on the current real-time reading data.
We have conducted another simple experiment on the same dataset (see Fig. 2), applied by Apriori algorithm, to illustrate a di erent useful type of learning output. Listing 1.1 describes the result obtained, which represents association rules between some entities. Such results can help in understating many or probably all risk cases, but more importantly, it can enhance the learning decision by avoiding false-positive actions. For example, rule (Cooker=False ==> Action=no) states explicitly that no action is required if the Cooker is o . Currently, this action could not be inferred by our dataset as no instance representing it. However, the association rules can be used as a validation (e.g., testing the accuracy of the inferred decision tree) by ltering out any false-positive action, generated imprecisely with the condition (e.g., Cooker==False).
Listing 1.1: Learning output which describe association rules between entities 1 . Cooker=F a l s e => S e n s o r 3=F a l s e . . . 2 . Ahmed smartphone=F a l s e => S e n s o r 3=F a l s e . . . 3 . d u r a t i o n=none => S e n s o r 3=F a l s e . . . 4 . d u r a t i o n=none => Cooker=F a l s e . . . 5 . Cooker=F a l s e => d u r a t i o n=none . . . 6 . Cooker=F a l s e => A c t i o n=no . . . 7 . d u r a t i o n=none => A c t i o n=no . . . 8 . Cooker=F a l s e d u r a t i o n=none => S e n s o r 3=F a l s e 9 . S e n s o r 3=F a l s e d u r a t i o n=none => Cooker=F a l s e 1 0 . S e n s o r 3=F a l s e Cooker=F a l s e => d u r a t i o n=none . . . . . . . . . 5
VII 6
In consonance with computable existing re-protection solutions, our IoT approach will be conceptually modelled to be complementary to the recent addressable risk detection devices for validation and evaluation only. Therefore, to generalise the main contributions of this approach, we will investigate empirically how our derivation technique of unexpected/smart actions can be customised to suit di erent IoT applications and speci cations.